A single biomarker is not adequate to identify patients with gastric cancer (GC) who have the potential to benefit from anti-PD-1/PD-L1 therapy, presumably owing to the complexity of the tumour microenvironment. The predictive value of tumour-infiltrating immune cells (TIICs) has not been definitively established with regard to their density and spatial organisation. Here, multiplex immunohistochemistry is used to quantify in situ biomarkers at sub-cellular resolution in 80 patients with GC. To predict the response to immunotherapy, we establish a multi-dimensional TIIC signature by considering the density of CD4+FoxP3−PD-L1+, CD8+PD-1−LAG3−, and CD68+STING+ cells and the spatial organisation of CD8+PD-1+LAG3− T cells. The TIIC signature enables prediction of the response of patients with GC to anti-PD-1/PD-L1 immunotherapy and patient survival. Our findings demonstrate that a multi-dimensional TIIC signature may be relevant for the selection of patients who could benefit the most from anti-PD-1/PD-L1 immunotherapy.
Immune checkpoint inhibitor (ICI)‐based immunotherapy brought new hope for gastric cancer (GC) treatment. However, due to the lack of proper biomarkers, patient selection and outcome prediction for GC's immunotherapy remain unsatisfying. In this study, through applying an extracellular vesicle (EV) protein expression array, we assessed the correlation of plasma EV‐derived protein spectrum with outcomes of ICI‐related therapeutic combinations. Plasma from 112 GC patients received ICI‐related therapies were investigated retrospectively/prospectively as three cohorts. We identified four plasma EV‐derived proteins (ARG1/CD3/PD‐L1/PD‐L2) from 42 crucial candidate proteins and combined them as an EV‐score that robustly predicting immunotherapeutic outcomes at baseline and dynamically monitoring disease progression along with treatment. High EV‐score reflected microenvironmental features of stronger antitumour immunity, characterized by more activated CD8 + T/NK cells, higher TH1/TH2 ratio and higher expressions of IFN‐γ/perforin/granzymes in paired peripheral blood, which were verified by dataset analysis and in vivo experiments. EV‐score≥1 GC received more therapeutic benefits from ICIs, while EV‐score < 1 GC potentially benefited more from ICIs combining HER2‐targeted therapies. Collectively, through proposing a plasma EV‐score on protein level that powerfully predicting and monitoring GC's immunotherapeutic outcomes, our work facilitated clinical patient selection and decision‐makings, and provided mechanistical insights for immunotherapy‐related microenvironmental changes and improvements for current ICI‐regimens.
Acquired Trastuzumab resistance is a complicated and disastrous event for HER2-positive gastric cancer (GC). In this study, we successfully established a GC PDX model with Trastuzumab sensitivity (176P) and induced a homologous model with acquired Trastuzumab resistance (176R), then comprehensively delineated the landscape of Trastuzumab resistance mechanisms using single-cell transcriptome sequencing, as well as protein profiling and genomic variation analysis. According to multi-omics study, different gene expression profiles, rather than genetic changes, contributed to acquired Trastuzumab resistance. The mechanisms underlying acquired Trastuzumab resistance present great complexity as multiple molecules and pathways were involved, including ERBB family, MAPK, PI3K/AKT, JAK/STAT, and cell cycle pathways. Through phenotypical and molecular validation, we found that Trastuzumab combined with HER3-targeted antibody or MEK inhibitor demonstrated excellent antitumor activity and good tolerance, which may serve as promising strategies for overcoming acquired Trastuzumab resistance.
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